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GHSA-q263-fvxm-m5mw

MEDIUMFix: tensorflow/tensorflow@0cc38aa

GHSA-q263-fvxm-m5mw is a medium-severity (CVSS 4.4) vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-q263-fvxm-m5mw is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Heap out of bounds access in MakeEdge in TensorFlow

Also known asBIT-tensorflow-2020-26271CVE-2020-26271PYSEC-2020-257PYSEC-2020-302PYSEC-2020-337
Published
Dec 10, 2020
Updated
Jul 8, 2026
Affected
15 pkgs
Patched
15 / 15
Exploits
1 known
Exploitation data as of Jul 8, 2026 · OSV.dev, FIRST.org (EPSS)

Real-World Exposure

15 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+7 more

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Impact

Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality:

  DataType src_out = src->output_type(output_index);
  DataType dst_in = dst->input_type(input_index);
  //...

However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays.

In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.

Patches

We have patched the issue in GitHub commit 0cc38aaa4064fd9e79101994ce9872c6d91f816b and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Affected Packages

15 total 15 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions1.15.5
🐍PyPItensorflow2.0.0&&< 2.0.42.0.4
🐍PyPItensorflow2.1.0&&< 2.1.32.1.3
🐍PyPItensorflow2.2.0&&< 2.2.22.2.2
🐍PyPItensorflow2.3.0&&< 2.3.22.3.2
🐍PyPItensorflow-cpuall versions1.15.5
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update tensorflow to 1.15.5 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-q263-fvxm-m5mw is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-q263-fvxm-m5mw is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-q263-fvxm-m5mw. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The [`MakeEdge` function](https://github.com/tensorflow/tensorflow/blob/3616708cb866365301d8e67b43b32b46d94b08a0/tensorflow/core/common_runtime/graph_constructor.cc#L1426-L1438) creates an edge between one output tensor of the `src` node (given by `output_index`) and the input slot of the `dst` node (given by `input_index`). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding `DataTyp
O3 Security · Impact-Aware SCA

Is GHSA-q263-fvxm-m5mw in your dependencies?

O3 detects GHSA-q263-fvxm-m5mw across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.